METHODS AND APPARATUS TO DETERMINE EXECUTION COST

    公开(公告)号:US20220091895A1

    公开(公告)日:2022-03-24

    申请号:US17541016

    申请日:2021-12-02

    Abstract: Methods, apparatus, systems, and articles of manufacture to determine execution cost are disclosed. An example apparatus includes memory; instructions included in the apparatus; and processor circuitry to execute the instruction to: cause a plurality of instructions corresponding to a mnemonic to be executed; determine an average execution cost of the plurality of instructions; determine a standard deviation of execution costs of the plurality of instructions; and generate a mapping table including an entry, the entry including the mnemonic in association with the average and the standard deviation.

    METHODS AND APPARATUS FOR MACHINE LEARNING-GUIDED COMPILER OPTIMIZATIONS FOR REGISTER-BASED HARDWARE ARCHITECTURES

    公开(公告)号:US20220121430A1

    公开(公告)日:2022-04-21

    申请号:US17561417

    申请日:2021-12-23

    Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that perform machine learning-guided compiler optimizations for register-based hardware architectures. Examples disclosed herein include a non-transitory computer readable medium comprising instructions that, when executed, cause a machine to at least select a register-based compiler transformation to apply to source code at a current position in a search tree, determine whether the search tree is in need of pruning based on an output of a query to a machine learning (ML) model, in response to determining the search tree is in need of pruning, prune the search tree at the current position, in response to applying the selected register-based compiler transformation to the source code, generate a code variant, calculate a score associated with the source code at the current position in the search tree, and update parameters of the machine learning (ML) model to include the calculated score.

    Methods and apparatus for machine learning-guided compiler optimizations for register-based hardware architectures

    公开(公告)号:US11954466B2

    公开(公告)日:2024-04-09

    申请号:US17561417

    申请日:2021-12-23

    CPC classification number: G06F8/443 G06N5/01

    Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that perform machine learning-guided compiler optimizations for register-based hardware architectures. Examples disclosed herein include a non-transitory computer readable medium comprising instructions that, when executed, cause a machine to at least select a register-based compiler transformation to apply to source code at a current position in a search tree, determine whether the search tree is in need of pruning based on an output of a query to a machine learning (ML) model, in response to determining the search tree is in need of pruning, prune the search tree at the current position, in response to applying the selected register-based compiler transformation to the source code, generate a code variant, calculate a score associated with the source code at the current position in the search tree, and update parameters of the machine learning (ML) model to include the calculated score.

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